1. Field of the Invention
The present invention relates to a method for course prediction in driver assistance systems for motor vehicles, in which method a dynamic course hypothesis is created on the basis of vehicle-dynamics data of the vehicle.
2. Description of Related Art
In driver assistance systems that assist the driver in driving the vehicle and/or warn him or her of acute hazards, initiate automatic actions to avert a collision hazard, or activate safety systems to prepare for the collision, it is often necessary to predict the anticipated course of the host vehicle. A typical example of a driver assistance system of this kind is a dynamic vehicle speed controller (adaptive cruise control, ACC), with which the speed of the host vehicle is automatically adjusted to the speed of a preceding vehicle that is localized with the aid of a radar or lidar system. The course prediction is then used principally to decide whether a sensed object is to be selected as a target object for distance regulation, or whether that object is an irrelevant object, for example a vehicle in an adjacent lane.
ACC systems of this kind are already successfully in use, but the field of application is so far limited mostly to driving on expressways or on well-constructed main roads. In these situations it is generally possible to limit the analysis of the traffic environment to moving targets, for example preceding vehicles, while stationary targets, for example immovable objects at the roadside, can be ignored. In such systems, it is primarily the present vehicle speed and the yaw rate of the host vehicle that are employed to predict the host-vehicle course. Based on these data, a course hypothesis is created by mathematically describing the centerline of the anticipated course as a parabola whose curvature is defined by the ratio between yaw rate and vehicle speed. This course hypothesis obtained from vehicle-dynamics data will be referred to here as a “dynamic course hypothesis.”
Efforts are being made to expand the applicability of ACC systems to other traffic situations, e.g., to stop-and-go situations on expressways, to driving on main roads, and ultimately also to driving in city traffic. In these situations, in which stationary targets generally must also be considered and the selection of valid target objects and the recognition of obstacles is thus substantially more complex, greater demands are also made in terms of course prediction accuracy.
It has already been proposed also to employ data from other information sources for the course prediction, for example the collective motion of other vehicles that can be sensed with the aid of the radar system, data from a navigation system, position data of stationary targets at the roadside, or even information supplied from a mono or stereo video system. Incorporation of this additional information into the course prediction has hitherto been based, however, on a rather casuistic approach, and improves the course prediction, if at all, in specific narrowly limited situations. The course prediction accuracy and reliability achievable in this fashion is therefore not sufficient for advanced driver assistance systems.